Delay Bound Relaxation with Deep Learning-based Haptic Estimation for Tactile Internet

TL;DR

This paper presents a deep learning model that estimates force feedback in haptic teleoperation, allowing relaxation of delay constraints and improving resource efficiency in Tactile Internet applications.

Contribution

It introduces a novel DL-based force estimation approach using multi-modal data and advanced neural network architectures to relax delay bounds in tactile internet systems.

Findings

Increases reliably served users by up to 66%.

Enables batching of multiple haptic packets, improving resource utilization.

Enhances network capacity and reliability in teleoperation scenarios.

Abstract

Haptic teleoperation typically demands sub-millisecond latency and ultra-high reliability (99.999%) in Tactile Internet. At a 1 kHz haptic signal sampling rate, this translates into an extremely high packet transmission rate, posing significant challenges for timely delivery and introducing substantial complexity and overhead in radio resource allocation. To address this critical challenge, we introduce a novel DL modelthat estimates force feedback using multi-modal input, i.e. both force measurements from the remote side and local operator motion signals. The DL model can capture complex temporal features of haptic time-series with the use of CNN and LSTM layers, followed by a transformer encoder, and autoregressively produce a highly accurate estimation of the next force values for different teleoperation activities. By ensuring that the estimation error is within a predefined threshold, the teleoperation system can safely relax its strict delay requirements. This enables the batching and transmission of multiple haptic packets within a single resource block, improving resource efficiency and facilitating scheduling in resource allocation. Through extensive simulations, we evaluated network performance in terms of reliability and capacity. Results show that, for both dynamic and rigid object interactions, the proposed method increases the number of reliably served users by up to 66%.